The release of the AVE dataset [1]} has stimulated research in audio-visual event detection. For example, an audio-guided visual attention mechanism is introduced in [1]} to learn which visual region to look at based on the visual and audio information. Lin et al. proposed to learn global and local event information in a sequence to sequence manner [3]}. Finally, Wu et al. extracted the global representation of one modality and found the local segments that are relevant to the event in the other modality and vice versa [4]}. In our work, we propose to better integrate audio-visual information by computing a global feature with an attention module to include only the relevant information present in the modalities. In addition, instead of visual spatial attention, we propose to use an attention mechanism on the audio feature maps based on the visual information, called modality conditioning. Indeed, we observed that the visual modality has more information to contribute to the audio path of MAFnet than the reverse.
